Xu Ronghui
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA.
Stat Med. 2003 Nov 30;22(22):3527-41. doi: 10.1002/sim.1572.
We generalize the well-known R(2) measure for linear regression to linear mixed effects models. Our work was motivated by a cluster-randomized study conducted by the Eastern Cooperative Oncology Group, to compare two different versions of informed consent document. We quantify the variation in the response that is explained by the covariates under the linear mixed model, and study three types of measures to estimate such quantities. The first type of measures make direct use of the estimated variances; the second type of measures use residual sums of squares in analogy to the linear regression; the third type of measures are based on the Kullback-Leibler information gain. All the measures can be easily obtained from software programs that fit linear mixed models. We study the performance of the measures through Monte Carlo simulations, and illustrate the usefulness of the measures on data sets.
我们将线性回归中著名的R(2) 度量推广到线性混合效应模型。我们的工作是受东部肿瘤协作组进行的一项整群随机研究的启发，该研究旨在比较两种不同版本的知情同意书。我们量化了线性混合模型下协变量所解释的响应变量的变异，并研究了三种估计此类数量的度量方法。第一类度量方法直接使用估计的方差；第二类度量方法类似于线性回归，使用残差平方和；第三类度量方法基于库尔贝克-莱布勒信息增益。所有这些度量都可以从拟合线性混合模型的软件程序中轻松获得。我们通过蒙特卡罗模拟研究了这些度量的性能，并在数据集上说明了这些度量的实用性。
